Sentiment Analysis on IMDb Movie Reviews

This project performs sentiment analysis on the IMDb movie reviews dataset using Python and machine learning techniques. The main script, sentiment_analysis_imdb.py, loads, preprocesses, and classifies movie reviews as positive or negative using Logistic Regression.

Features

• Loads IMDb dataset from local folders (positive and negative reviews)
• Text preprocessing: lowercasing, stopword removal, stemming
• Feature extraction using TF-IDF vectorization
• Label encoding for sentiment classes
• Model training and evaluation using Logistic Regression
• Plots and saves confusion matrix and precision/recall/F1-score bar charts

Requirements

• Python 3.7+
• pandas
• numpy
• scikit-learn
• nltk
• matplotlib
• seaborn

Install dependencies with:

pip install pandas numpy scikit-learn nltk matplotlib seaborn

Dataset

Download the IMDb Large Movie Review Dataset and extract it. Update the script if your dataset path differs from ./aclImdb/train and ./aclImdb/test.

Usage

1. Ensure the dataset is extracted and the directory structure matches the script's expectations.
2. Run the script:

   python sentiment_analysis_imdb.py

3. The script will output model metrics and save plots:
   • confusion_matrix_lr.png: Confusion matrix for Logistic Regression
   • precision_recall_f1_lr.png: Precision, Recall, and F1-Score bar chart

Output

• Console output: Accuracy, F1 Score, classification report
• Images: Confusion matrix and metrics bar chart saved in the project directory